class_docstr: A data iterator object to contain a dynamic graph with a
  changing edge set and weights . The feature set and node labels
  (target) are also dynamic. The iterator returns a single discrete temporal
  snapshot for a time period (e.g. day or week). This single snapshot is a
  Pytorch Geometric Data object. Between two temporal snapshots the edges,
  edge weights, target matrices and optionally passed attributes might change.
  
  Args:
      edge_indices (Sequence of Numpy arrays): Sequence of edge index tensors.
      edge_weights (Sequence of Numpy arrays): Sequence of edge weight tensors.
      features (Sequence of Numpy arrays): Sequence of node feature tensors.
      targets (Sequence of Numpy arrays): Sequence of node label (target) tensors.
      **kwargs (optional Sequence of Numpy arrays): Sequence of additional attributes.
class_docstr_tok: ["A","data","iterator","object","to","contain","a","dynamic","graph","with","a","changing","edge","set","and","weights",".","The","feature","set","and","node","labels","(","target",")","are","also","dynamic",".","The","iterator","returns","a","single","discrete","temporal","snapshot","for","a","time","period","(","e.g",".","day","or","week",")",".","This","single","snapshot","is","a","Pytorch","Geometric","Data","object",".","Between","two","temporal","snapshots","the","edges",",","edge","weights",",","target","matrices","and","optionally","passed","attributes","might","change",".","Args",":","edge_indices","(","Sequence","of","Numpy","arrays",")",":","Sequence","of","edge","index","tensors",".","edge_weights","(","Sequence","of","Numpy","arrays",")",":","Sequence","of","edge","weight","tensors",".","features","(","Sequence","of","Numpy","arrays",")",":","Sequence","of","node","feature","tensors",".","targets","(","Sequence","of","Numpy","arrays",")",":","Sequence","of","node","label","(","target",")","tensors",".","*","*","kwargs","(","optional","Sequence","of","Numpy","arrays",")",":","Sequence","of","additional","attributes","."]
code: def __getitem__(self, time_index: Union[int, slice]):
      if isinstance(time_index, slice):
          snapshot = DynamicGraphTemporalSignal(
              self.edge_indices[time_index],
              self.edge_weights[time_index],
              self.features[time_index],
              self.targets[time_index],
              **{
                  key: getattr(self, key)[time_index]
                  for key in self.additional_feature_keys
              },
          )
      else:
          x = self._get_features(time_index)
          edge_index = self._get_edge_index(time_index)
          edge_weight = self._get_edge_weight(time_index)
          y = self._get_target(time_index)
          additional_features = self._get_additional_features(
              time_index
          )
  
          snapshot = Data(
              x=x,
              edge_index=edge_index,
              edge_attr=edge_weight,
              y=y,
              **additional_features,
          )
      return snapshot
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/signal/dynamic_graph_temporal_signal.py
imports: import torch
  import numpy
  from typing import Sequence, Union
  from torch_geometric.data import Data
label_desc: Use image node_id 10 for calling the DynamicGraphTemporalSignal obj's underlying member method code with example usage: obj.__getitem__(time_index) and returns: snapshot